Academic Integrity Decision Framework

Overview

A practical decision framework for staff assessing student generative-AI use. Instead of asking the unanswerable question “did the student use AI?”, the framework structures the decision around six answerable questions that map to the concepts integrity decisions actually depend on: permission, disclosure, contribution, verification, authorship and honesty.

Problem

Integrity decisions about AI use are frequently made ad hoc: different staff weigh different factors, similar cases receive different outcomes, and students cannot predict how their behaviour will be judged. Detection tools cannot fix this because the problem is conceptual, not forensic — institutions lack a shared structure for reasoning from facts to decision.

My role

Developed the framework from my policy-analysis findings and the policy-to-decision consistency study, and tested it against the fictional case portfolio published on this site.

Research question

Can a small, fixed set of questions structure AI-related integrity decisions so that they are consistent across decision-makers, explainable to students and proportionate to the actual breach?

The framework

Every case is assessed through six questions, in order:

1. Was AI use permitted? — What did the applicable policy and task instructions actually allow?
2. Was it disclosed? — Did the student acknowledge the use as required?
3. Did AI assist or replace the student’s work? — Where did the substantive intellectual contribution come from?
4. Did the student verify the output? — Did the student check accuracy, sources and reasoning, or submit unexamined text?
5. Was authorship preserved? — Can the student explain, defend and take responsibility for the submitted work?
6. Was there deception? — Did the student misrepresent how the work was produced?

The pattern of answers — not any single answer — determines the outcome category and proportionate response.

Method

Conceptual synthesis from policy analysis and integrity literature, followed by iterative testing: each version of the framework was applied to the fictional case set, and questions were revised where they failed to discriminate between cases or produced unfair results.

Tools

Policy corpus from the Australian policy-analysis project, fictional case vignettes, structured worksheets for applying the framework.

Findings

Claims about the framework’s performance are reported only once verified.

• [Add verified finding here]

Outputs

• The framework itself (this page and the applied examples in the Case Portfolio)
• Staff-facing guidance based on the framework — see Technical Writing
• [Add workshop, report or paper details when confirmed]

Impact

• [Add verified impact here — e.g. adoption, feedback, citations]

Limitations

• The framework structures judgement; it does not replace it. Two assessors can answer the six questions differently.
• It assumes a policy exists against which “permitted” can be answered; where policy is silent, the framework surfaces the gap but cannot fill it.
• Tested to date on fictional cases only.

Lessons learned

• Separating disclosure failures from authorship failures prevents the most common unfairness: treating honest, permitted use that was poorly acknowledged as if it were contract cheating.
• [Add further lessons here]

Related publication

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Downloadable materials

• [Add a printable framework worksheet to public/downloads/ and list it in the frontmatter downloads field]